Print

COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
ENM 507HEURISTIC METHODS AND APPLICATIONS3 + 02nd Semester7,5

COURSE DESCRIPTION
Course Level Doctorate Degree
Course Type Elective
Course Objective A large part of the research area of industrial engineering includes NP-hard problems. These problems usually can not be solved by exact optimization techniques. In recent years, heuristic techniques will be effectively deal with these problems. In this course, heuristic techniques and its application areas will be introduced.
Course Content Introduction to Optimization problems, NP-Complete problems, Lagrangean Relaxation and Lagrangean Heuristics, Classical Construction Heuristics (Savings, Nearest Neighbor, Greedy) Classical Improvement Heuristics (Node Insertion, k-opt, or-opt), Meta-heuristic Methods (Genetic Algorithms, Tabu Search, Simulated Annealing, Ant Colony)
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Student learns the basic concepts of heuristic methods
2Student gains the ability of identificating problems and finding solutions by using a mathematical model.
3Student gains the ability of improving classical and heuristic methods for the solution of NP-Hard problems.

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10
LO 0013311411112
LO 0025545311113
LO 0035554321112
Sub Total131310101043337
Contribution4433311112

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)13113
Assignments5420
Mid-terms13535
Final examination14545
Report / Project14040
Total Work Load

ECTS Credit of the Course






195

7,5
COURSE DETAILS
 Select Year   


This course is not available in selected semester.


Print

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes